Human handedness in interactive situations: Negative perceptual frequency effects can be reversed!
Bibliographic record
Abstract
Left-handed performers seem to enjoy an advantage in interactive sports. Researchers suggest this is predominantly due to the relative scarcity of left-handers compared with right-handers. Such negative frequency-dependent advantages are likely to appear in inefficient game-play behaviour against left-handed opponents such as reduced ability to correctly anticipate left-handers' action intentions. We used a pre-post retention design to test whether such negative frequency-dependent perceptual effects can be reversed via effective training. In a video-based test, 30 handball novices anticipated the shot outcome of temporally occluded handball penalties thrown by right- and left-handed players. Between the pre- and post-tests, participants underwent a perceptual training programme to improve prediction accuracy, followed by an unfilled retention test one week later. Participants were divided into two hand-specific training groups (i.e. only right- or left-handed shots were presented during training) and a mixed group (i.e. both right- and left-handed shots were presented). Our results support the negative frequency-dependent advantage hypothesis, as hand-specific perceptual training led to side-specific improvement of anticipation skills. Similarly, findings provide experimental evidence to support the contention that negatively frequency-dependent selection mechanisms contributed to the maintenance of the handedness polymorphism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".